Searchable Encryption for Biometric Identification Revisited

  • Ghassane Amchyaa
  • Julien Bringer
  • Roch Lescuyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9963)


Cryptographic primitives for searching and computing over encrypted data have proven useful in many applications. In this paper, we revisit the application of symmetric searchable encryption (SSE) to biometric identification. Our main contribution is two SSE schemes well-suited to be applied to biometric identification over encrypted data. While existing solution uses SSE with single-keyword search and highly sequential design, we use threshold conjunctive queries and parallelizable constructions. As a result, we are able to perform biometric identification over a large amount of encrypted biometric data in reasonable time. Our two SSE schemes achieve a different trade-off between security and efficiency. The first scheme is more efficient, but is proved secure only against non-adaptive adversaries while the second is proved secure against adaptive adversaries.



This work has been partially funded by the French ANR-12-CORD-0014 project SECULAR and the European H2020 TREDISEC project under the Grant Agreement 644412.


  1. 1.
    Adjedj, M., Bringer, J., Chabanne, H., Kindarji, B.: Biometric identification over encrypted data made feasible. In: Prakash, A., Sen Gupta, I. (eds.) ICISS 2009. LNCS, vol. 5905, pp. 86–100. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Boldyreva, A., Chenette, N.: Efficient fuzzy search on encrypted data. In: Cid, C., Rechberger, C. (eds.) FSE 2014. LNCS, vol. 8540, pp. 613–633. Springer, Heidelberg (2015)Google Scholar
  3. 3.
    Bringer, J., Chabanne, H., Kindarji, B.: Identification with encrypted biometric data. Secur. Commun. Netw. 4(5), 548–562 (2011)CrossRefGoogle Scholar
  4. 4.
    Bringer, J., Chabanne, H., Patey, A.: Practical identification with encrypted biometric data using Oblivious RAM. In: ICB 2013, pp. 1–8. IEEE (2013)Google Scholar
  5. 5.
    Cash, D., Jaeger, J., Jarecki, S., Jutla, C.S., Krawczyk, H., Rosu, M., Steiner, M.: Dynamic searchable encryption in very-large databases: data structures and implementation. In: NDSS 2014. The Internet Society (2014)Google Scholar
  6. 6.
    Cash, D., Jarecki, S., Jutla, C., Krawczyk, H., Roşu, M.-C., Steiner, M.: Highly-scalable searchable symmetric encryption with support for boolean queries. In: Canetti, R., Garay, J.A. (eds.) CRYPTO 2013, Part I. LNCS, vol. 8042, pp. 353–373. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Curtmola, R., Garay, J.A., Kamara, S., Ostrovsky, R.: Searchable symmetric encryption: improved definitions and efficient constructions. In: CCS 2006, pp. 79–88. ACM (2006)Google Scholar
  8. 8.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  9. 9.
    Apache Hadoop Project.
  10. 10.
    Huang, Y., Malka, L., Evans, D., Katz, J.: Efficient privacy-preserving biometric identification. In: NDSS 2011. The Internet Society (2011)Google Scholar
  11. 11.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. 14(1), 4–20 (2004)CrossRefGoogle Scholar
  12. 12.
    Li, J., Lin, X., Zhang, Y., Han, J.: KSF-OABE: outsourced attribute-based encryption with keyword search function for cloud storage. IEEE Trans. Serv. Comput. PP(99), 1 (2016)Google Scholar
  13. 13.
    Melchor, C.A., Fau, S., Fontaine, C., Gogniat, G., Sirdey, R.: Recent advances in homomorphic encryption: a possible future for signal processing in the encrypted domain. IEEE Sig. Process. Mag. 30(2), 108–117 (2013)CrossRefGoogle Scholar
  14. 14.
    Ostrovsky, R.: Efficient computation on oblivious rams. In: ACM Symposium on Theory of Computing - STOC 1990, pp. 514–523. ACM (1990)Google Scholar
  15. 15.
    Ostrovsky, R., Skeith, W.E.: A survey of single-database private information retrieval: techniques and applications. In: Okamoto, T., Wang, X. (eds.) PKC 2007. LNCS, vol. 4450, pp. 393–411. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-71677-8_26 CrossRefGoogle Scholar
  16. 16.
    Wang, J., Shen, H.T., Song, J., Ji, J.: Hashing for similarity search: a survey. In: CoRR, abs/1408.2927 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ghassane Amchyaa
    • 1
  • Julien Bringer
    • 1
  • Roch Lescuyer
    • 1
  1. 1.Safran Identity and SecurityIssy-Les-MoulineauxFrance

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